Stone
PRichards
MA’Hern
RHardy
J A study to investigate the prevalence, severity and correlates of fatigue among patients with cancer in comparison with a control group of volunteers without cancer. Ann Oncol 2000;11561- 567PubMedGoogle ScholarCrossref

Burns
CMTijhuis
MASeidell
JC The relationship between quality of life and perceived body weight and dieting history in Dutch men and women. Int J Obes Relat Metab Disord 2001;251386- 1392PubMedGoogle ScholarCrossref

BackgroundFatigue is a common symptom, even among healthy individuals, but little is understood about it. We examined the associations among adiposity, depressive symptoms, cytokine levels, and multidimensional fatigue symptoms in otherwise healthy subjects. Furthermore, we examined whether obesity would account for a significant portion of fatigue and, if so, what kind of fatigue complaints appear to be related to obesity.

MethodsSeventy healthy subjects (36 women and 34 men) with an average age of 36.0 years and at less than or equal to 170% of ideal body weight participated in the study. Participants had their height, weight, neck circumference, ratio of the waist to hip circumference, percentage of body fat, and plasma interleukin 6 and soluble intercellular adhesion molecule 1 concentrations measured. Their sleep was monitored with an overnight polysomnogram, and subjects completed the short form of the Multidimensional Fatigue Symptom Inventory, which measures 5 domains of fatigue, and the Center for Epidemiologic Studies–Depression Scale.

ResultsObesity, as measured by body mass index (calculated as weight in kilograms divided by the square of height in meters) and percentage of body fat, was associated with general fatigue. Depression scores were significantly related to all subscales of fatigue; the highest correlation was shown with emotional fatigue. The effect of obesity on physical fatigue was significant, even after controlling for depression. In general, interleukin 6 and soluble intercellular adhesion molecule-1 levels were unrelated to measures of fatigue.

ConclusionsObesity, cytokine concentrations, and depressive symptoms explained different dimensions of fatigue as measured by the short form of the Multidimensional Fatigue Symptom Inventory. Obesity accounted for a significant portion of physical fatigue after controlling for depressive symptoms and circulating levels of interleukin 6 and soluble intercellular adhesion molecule-1.

Fatigue is a common complaint in healthy individuals as well as in numerous patient groups such as those with depression, rheumatoid disorders, multiple sclerosis, congestive heart failure, and cancer. Curiously, little is understood about its symptomatology, biological mechanisms, and treatment.1 Furthermore, fatigue is difficult to classify by etiology; as a subjective experience, it is identifiable only by patients’ self-reports.2,3 Fatigue is conceptually ambiguous and may manifest itself with physical/neuromuscular, emotional/affective, and mental/cognitive symptoms.4,5 The multiple ways in which fatigue may be expressed suggest that it might be measured by assessing a range of symptom domains. Various newer tools, such as the Multidimensional Fatigue Symptom Inventory (MFSI), tap into this multidimensionality.6

Fatigue is a common symptom in mood disorders and is one of the key diagnostic symptoms of major depressive disorder, bipolar disorder, and dysthymic disorder.7 Ax et al8 reported that 24% to 80% of patients with chronic fatigue syndrome may have concurrent depression, whereas Judge et al9 found that more than two thirds of patients with depression present with signs of fatigue. Investigators have also suggested that fatigue and depression are overlapping conditions that may share similar underlying mechanisms.10,11

Fatigue is also a frequent complaint in obese people. Excessive body fat has adverse consequences on the immune system, mediated via increased levels of circulating inflammatory cytokines.12,13 Concentrations of the proinflammatory cytokines tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), which are associated with sleepiness and fatigue, are elevated in sleep apnea and obesity and might play a role in the pathogenesis and pathological sequelae of both disorders.14 Similarly, the soluble form of intercellular adhesion molecule 1 (sICAM-1) is positively associated with body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters), ratio of the waist to hip circumference (WHR), and plasma TNF-α activity.15 Moreover, fatigue in obesity can be caused by increased sleep problems, comorbid medical illness, psychiatric disorders, and unexplored metabolic processes related to obesity per se beyond inflammatory cytokine levels.16

There are several methods of assessing body composition. Although BMI accurately predicts obesity-related morbidity and mortality,17 the BMI formula may erroneously label a muscular person as obese.18 Bioelectrical impedance measurements can avoid this problem by specifically calculating the percentage of body fat and lean mass by using resistance ohms.19 Aside from absolute body weight and percentage of body fat, the distribution of fat is also important because visceral fat above the waist is associated with several illnesses.20

Community and primary care studies have shown that fatigue is common in the general population,21-27 and these results imply that there is a spectrum of fatigue symptoms even among healthy individuals. However, the prevalence estimates of fatigue in the general population have been based mostly on single-item measures or questionnaires without considering diverse domains of fatigue symptoms. Similarly, previous studies of fatigue and its relationship with depression10,28-35 or obesity14,36 did not take the diversity of fatigue symptomatology seriously. Few studies have examined the relationships among fatigue, obesity, and depressive symptoms.

We examined the associations among obesity, depressive symptoms, cytokine levels, and multidimensional fatigue symptoms as measured by the MFSI among asymptomatic and healthy individuals. We hypothesized that obesity, cytokine levels, and depressive symptoms may contribute to different dimensions of fatigue as measured by the MFSI. Furthermore, we hypothesized that obesity would account for a significant portion of various fatigue dimensions differently, even after controlling for depressive symptoms and circulating levels of IL-6 and sICAM-1.

Methods

Subjects

Seventy subjects (36 women and 34 men) were recruited through advertisements and word of mouth referral. Subjects were studied after obtaining written informed consent, which was approved by the University of California–San Diego Institutional Review Board.

We limited enrollment to subjects aged 25 to 50 years. Participants at greater than 170% of ideal body weight were excluded because of the possibility of confounding by other conditions associated with obesity.37 Subjects were excluded if they had a history of sleep disorder, heart disease, liver or renal disease, diabetes, psychosis, severe asthma, or cerebrovascular disease. Pregnancy and current intake of prescription medication except for antihypertensives were additional exclusion criteria.

Participants receiving antihypertensives had their treatment tapered slowly in 2 to 3 steps, depending on the patient’s regular dosage. A 3-week drug washout period was observed before studying the patients. Individuals whose blood pressure exceeded 180/110 mm Hg were returned to active treatment and were not studied in this protocol. Participants had their sleep monitored for an entire night in the Clinical Research Center with standard polysomnography using the Embla polysomnograph (Flaga Inc, Reykjavik, Iceland). Patients whose respiratory disturbance index, that is, [total number of respiratory events (apnea + hypopnea)/total sleep time], was greater than 15 were excluded because of likely obstructive sleep apnea.

Anthropometric data measures

Subjects were instructed to remove their shoes, articles in their pockets, and exterior clothing other than a light shirt and pants. Weight was measured to the nearest 0.1 kg, and height was assessed to the nearest 0.1 cm and together these values were used to compute the BMI. Waist circumference (measured at the narrowest point superior to the hip) was divided by the circumference of the hip (measured at its greatest gluteal protuberance) to obtain the WHR, which was used as a measure of abdominal obesity.

The percentage of body fat was measured by a bioelectrical impedance system and Cyprus version 1.2 body composition analysis software (RJL Systems, Milford, Conn). Formulas using resistance ohms were used to calculate lean mass; and lean mass with body weight (in kilograms) was used to calculate the percentage of body fat.

Psychological evaluation

Participants completed the short form of the MFSI (MFSIsf).6 The MFSIsf is a 30-item self-report measure designed to assess the principal manifestations of fatigue. It contains 6 items on each of 5 subscales (general fatigue, physical fatigue, emotional fatigue, mental fatigue, and vigor). Items are rated on a 5-point scale indicating how true each statement was for the respondent during the last week (0 indicates not at all; 4, extremely).

Participants also completed the Center for Epidemiologic Studies–Depression Scale (CES-D). The CES-D is a frequently used self-report scale that has been shown to be reliable and valid for assessing depressive symptoms.38 The CES-D includes 20 questions, and scores of greater than 16 are commonly interpreted as denoting high levels of depressive symptoms.

Chemical assay

Blood was drawn via saline lock around 6
AM. Circulating levels of IL-6 and the concentration of sICAM-1 in plasma were measured using an enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, Minn). The precision and sensitivity performance values for IL-6 were intra-assay coefficient of variation of 2.0%, interassay coefficient of variation of 3.8%, and sensitivity of less than 0.70 pg/mL. For sICAM-1, the precision and sensitivity performance values were intra-assay coefficient of variation of 4.6%, interassay coefficient of variation of 6.4%, and sensitivity of less than 0.35 ng/mL.

Statistical analysis

We analyzed data with Pearson correlation, hierarchical linear regression, and independent t test using SPSS version 11.0 software (SPSS Inc, Chicago, Ill). In separate multiple linear regression analyses, each MFSIsf subscale was the dependent variable. At step 1, we forced the entry of cytokines (IL-6 and sICAM-1). At step 2, we entered the CES-D score. At step 3, we entered obesity indices (WHR and percentage of body fat). Because there was a significant correlation between BMI and the percentage of body fat, to avoid the problem of multicollinearity and to evaluate the effect of fat itself, we chose the percentage of body fat as an obesity index and did not incorporate BMI in the multiple regressions. As a marker for fat distribution, we choose WHR and disregarded neck circumference, again to avoid multicollinearity.

To limit type I error, statistics were considered significant at P<.01 for the Pearson correlations.

Results

Subjects included 34 men and 36 women with an average age of 36.0 years. The mean BMI was 25.96 (range, 17.7-42.7), and mean percentage of body fat was 27.6% (range, 7.3%-51.7%). Other clinical characteristics of the subjects are presented in Table 1.

Table 2 summarizes the Pearson correlation coefficients (or t test in the case of sex) among the variables of interest. Sex was not associated with the MFSIsf fatigue subscales. Scores on the CES-D were significantly related to all measures of fatigue, and correlation coefficients were particularly high for MFSIsf emotional fatigue subscale (r = 0.702; P<.01).

The WHR was not significantly related to any MFSIsf subscale. Body mass index was significantly related to MFSIsf general fatigue subscale (r = 0.352; P<.01) but was not significantly related to the other fatigue subscales. The percentage of body fat was also significantly related to MFSIsf general fatigue subscale (r = 0.322; P<.01) but was not significantly related to the other fatigue subscales. Cytokine levels were unrelated to any of the fatigue subscales.

We then analyzed these data with hierarchical linear regression to ascertain if obesity indices contributed additional information to fatigue, after controlling for concentrations of cytokines and depressive symptoms (CES-D). In separate multiple linear regression analyses, each MFSIsf subscale was the dependent variable. At step 1, we forced the entry of cytokines (IL-6 and sICAM-1). At step 2, we entered the CES-D. At step 3, we entered the obesity indices (WHR and percentage of body fat). In Table 3, final models for each MFSIsf subscale are presented.

For the analysis of MFSIsf general fatigue as the dependent variable, at step 1, we entered IL-6 and sICAM-1 levels. The 2 inflammatory markers did not account for the MFSIsf general fatigue subscale scores. At step 2, the CES-D scores significantly accounted for 21.4% of the MFSIsf general fatigue subscale variance (P<.01). At step 3, obesity measures (WHR and percentage of body fat) did not account for a significant amount of the variance in MFSIsf general fatigue subscale, after controlling for IL-6 and sICAM-1 levels and depression (Table 3).

The inflammatory markers together did not explain a significant proportion of variance in MFSIsf physical fatigue subscale. When the CES-D scores were added to the model at step 2, they accounted significantly for an additional 8.7% of the variance (P<.05). At step 3, we entered the obesity indices (WHR and percentage of body fat). After controlling for IL-6 and sICAM-1 levels plus the CES-D scores, obesity measures accounted for an additional 8.7% of the physical fatigue subscale variance (P = .03) (Table 3).

When we entered MFSIsf emotional fatigue as a dependent variable, 42.6% of MFSIsf emotional fatigue was explained by CES-D scores (P<.01). Levels of the 2 cytokines and obesity indices could not explain MFSIsf mental fatigue subscale significantly, after controlling for depressive symptoms (Table 3). Neither IL-6 and sICAM-1 levels nor obesity indices were significantly associated with the MFSIsf mental fatigue subscale after controlling for depressive symptoms (Table 3). In terms of MFSIsf vigor, the CES-D scores accounted for 12.8% of the variance in step 2 (P<.05), but neither IL-6 and sICAM-1 levels nor obesity measures accounted for the additional variance of MFSIsf vigor (Table 3).

Comment

The conceptual borders of fatigue are ill defined and constitute often nebulous areas such as tiredness, excessive daytime sleepiness, reduced vitality, decreased strength, lack of energy, lethargy, difficulty with concentration, and short-term memory disturbance. Therefore, it is vital to recognize and assess the multidimensionality of fatigue. Our results showed that physical fatigue is the domain of fatigue most significantly related to obesity, although through undefined mechanisms.1

We found that obesity indices, as measured by BMI and the percentage of body fat, were correlated with the general fatigue subscale. The effect of the percentage of body fat on MFSIsf physical fatigue subscale was significant even after controlling for the CES-D scores and levels of inflammatory markers IL-6 and sICAM-1. These findings suggest that obesity contributes to subjective physical fatigue beyond the level explained by depressive mood and levels of IL-6 and sICAM-1.

Simple univariate correlations revealed that the percentage of body fat and BMI were significantly related to MFSIsf general fatigue. Not surprisingly, the CES-D scores were significantly related to all subscales of fatigue, with the highest correlation coefficients shown with the MFSIsf subscale emotional fatigue. The relationship between CES-D scores and fatigue held even in our population of healthy subjects.38,39 The results of a study of such relationships in patients with depressive disorders would be of interest.

It is estimated that more than 60% of men and 50% of women are currently overweight in the United States.40 Being overweight is significantly associated with reduced satisfaction with general health, physical functioning, and vitality in the general population.41 Excessive daytime sleepiness is a frequent complaint among obese people, even for those in whom medical illnesses have yet to develop.42

Among individuals with obesity, there is a subgroup of people for whom obesity is concomitant with chronic and low-grade inflammation, although they do not have any symptom of infection.43 A number of researchers reported elevated levels of TNF-α, IL-1, and IL-6 in obese people.44-46 Vgontzas and colleagues47 suggested that obesity-related daytime sleepiness may be a manifestation of a metabolic and/or circadian abnormality or that increased TNF-α and IL-6 levels might play a significant role in mediating fatigue in obese individuals. In addition, sICAM-1 levels have been positively associated with BMI, WHR, and plasma TNF-α levels.15

We did not find significant relationships between fatigue and circulating IL-6 or sICAM-1 levels. The absence of a consistent relationship between fatigue and these 2 inflammatory markers may be owing to a relatively small sample size or because we relied on a single-time blood sampling for inflammatory molecules. Assessment of inflammatory molecule levels at multiple time points may provide more comprehensive information on the relationship between fatigue and inflammatory marker levels. Similarly, it is possible that different findings may emerge in studies of patients with severe obesity.

Because of the cross-sectional nature of the study, we could not determine the direction of causality between obesity indices and fatigue. In addition, it is unclear by which mechanisms fatigue and obesity are associated in otherwise healthy subjects.

Central levels of cytokines may have more direct implications for feeling fatigued. However, in humans, associations between circulating cytokine levels and mood, depression, or sleep have been shown in many studies.14,48-50 In addition, given the growing evidence of polypeptide or protein crossing the blood-brain barrier from the peripheral blood compartment,51 we believe that it is meaningful and biologically relevant to assess circulating levels of inflammatory molecules in relation to fatigue.

Thirty-four (48%) of our subjects were overweight or obese, and the scores of the MFSIsf subscales in our sample were comparable to the scores of MFSIsf subscales in healthy control subjects.6 We might predict that the relationship between obesity and fatigue or inflammatory markers would be stronger when measured in subjects with more severe obesity or more severe fatigue symptomatology. In particular, because abdominal fat stores are uniquely rich sites in cytokines, the links among cytokine levels, fatigue, and obesity may be more evident in a substantially more obese sample.

For the assessment of obesity, we used BMI, neck circumference, WHR, and percentage of body fat. Although the percentage of body fat accurately predicts obesity-related morbidity and mortality,52 alternative measures for fat could provide important clinical implications. For instance, future studies may find it useful to quantify abdominal obesity with techniques such as computed tomography53,54 or biopsy of visceral fat.

Our results show that obesity explains a significant portion of physical fatigue even after controlling for depressive symptoms and inflammatory markers. Obesity is a well-known key factor in the metabolic syndrome, which threatens the endothelium of the cardiovascular system, skeletal muscle, adipose tissue, and liver.55 All of those sites could contribute to the relationship between physical fatigue and obesity to some extent. We speculate that there is a vicious cycle between the obese person’s metabolism and fatigue. Obesity per se leads to fatigue, and fatigue plays an important role in weight management by influencing physical activity levels. Future studies are needed that investigate ways to prevent or interrupt this vicious cycle.

Conclusions

We hypothesized that obesity, circulating inflammatory cytokine levels, and depressive symptoms could explain different dimensions of fatigue as measured by the MFSIsf. Depressive symptoms were correlated with all measures of fatigue. Obesity indices, as measured by the BMI and the percentage of body fat, were correlated with the general fatigue subscale. In particular, after controlling for depressive symptoms and the circulating levels of IL-6 and sICAM-1, obesity per se accounted for a significant portion of physical fatigue symptoms.

Stone
PRichards
MA’Hern
RHardy
J A study to investigate the prevalence, severity and correlates of fatigue among patients with cancer in comparison with a control group of volunteers without cancer. Ann Oncol 2000;11561- 567PubMedGoogle ScholarCrossref

Burns
CMTijhuis
MASeidell
JC The relationship between quality of life and perceived body weight and dieting history in Dutch men and women. Int J Obes Relat Metab Disord 2001;251386- 1392PubMedGoogle ScholarCrossref